Christopher S. McMahan

This study identifies genetic and phenotypic factors contributing to stalk lodging resistance in maize by analyzing intermediate traits and their genetic correlations.

This paper introduces a physics-informed machine learning framework for infectious disease forecasting, showing improved accuracy in predicting case and hospitalization counts using a hybrid model of differential equations and machine learning.

This study improves genomic prediction of stalk lodging resistance in maize by integrating multi-environment data, showing that multi-environment models significantly enhance prediction accuracy for most traits.

This study identifies novel metabolic and genetic factors associated with leaf senescence in maize through temporal analysis of physiological and metabolic changes.

The paper shows that alcohol drinking increases calcium activity in mouse central amygdala dynorphin-expressing neurons compared to other solutions.

The paper identifies 11 intermediate phenotypes related to stalk lodging resistance in maize and develops a high-density phenotypic dataset to enable genetic analysis and improve resistance.

The paper identifies dysregulated proteins in the orbitofrontal cortex of individuals with alcohol use disorder using proteomics and machine learning, linking them to drinking behavior in mice.

The paper shows that prelimbic cortical neurons encode hedonic value during solution consumption, with distinct activity patterns for water, ethanol, and sucrose, and these patterns are altered by ethanol dependence.

This paper develops models to predict nicotine metabolism ratios using genotypes and individual characteristics across different ancestries.

The paper identifies genetic risk factors for colorectal cancer in Indonesians through a genome-wide association study and polygenic risk modeling.